Media advertising accounts for a significant portion of the total marketing spend in a number of geographic markets, including the United States. Typically, media advertisements are marketed on the basis of, among other things, estimated reach—the total number of different people or households exposed, at least once, to a television channel (network) or a website page, software application, etc., during a given period of time. Historically, estimated reach has been determined by recording viewing activities of a sample population of a given medium's audience and extrapolating the sample results to forecast and measure behaviors of larger audiences. Advertising time slots during programming that is forecast to attract a large number of viewers is then typically sold at higher prices per unit than time slots during programming that is forecast to attract fewer viewers.
From the standpoint of television networks, website publishers, consumer electronics device manufacturers and others that are seeking to sell advertising space, accurate viewership forecasts are important so that potential revenue opportunities are not missed and demands for refunds by advertisers (when actual viewership fails to live up to its projected forecast) are minimized. From the standpoint of the advertisers, accurate viewership measurements are needed in order to ensure that advertising funds were properly allocated in ways designed to maximize their return.
As the number of available media channels and the variety of media programming has increased, the ability to accurately predict media viewership has become more and more challenging. In the case of television advertising, increased numbers of television channels, along with a rising number and variety of programs which populate those channels, have spawned ever increasing numbers of available programs for consumers to view and available advertising time slots which now must be considered for purchase by advertisers and sale by television networks. New methods of distribution for television programming such as streaming on websites such as Hulu™, “over-the-top” devices such as Boxxee™, Apple TV™ and iPads™—which deliver content over the top of the traditional cable company or satellite TV service provided set top boxes directly to consumer television sets via software or an alternate hardware device—are creating new kinds of viewing events and advertising inventory. However, direct viewing activity data is not always available for all of the channel, program and advertisement combinations. Even where such data exists, by itself it may be an insufficient basis for accurate viewership forecasting. For example, lack of reliable audience samples using existing estimation methods for some networks may result in inaccurate predictions of viewership for those networks. Additionally, some networks are not measured at all. The inaccuracies of viewership prediction are compounded when viewership includes multiple media. For example, existing methods of prediction of television cannot be transformed to accurately predict viewership spanning television, websites, consumer electronic devices and software applications. Accordingly, what are needed are improved methods and systems for media viewership forecasting.